Learning Vector Quantization - LVQ

Concept and definition

Learning Vector Quantization - LVQ

What is Learning Vector Quantization - LVQ?

Learning Vector Quantization (LVQ) is a prototype-based supervised sorting algorithm invented by Teuvo Kohonen, which falls under artificial intelligence algorithms.

It can be understood as a special case of artificial neural network in which a set of prototypes are initialized and fitted through the training data set.

This adjustment is based on the distance measure, just like the k-NN classifier, and LVQ tries to optimize the results of the k-NN classifier, specifically its 1-NN version. Thus, by means of a learning 

competitive, the prototype closest to the training sample is the one that will be updated, moving closer or farther away as it favors the classification results.

Reference :T. Kohonen. Learning vector quantization for pattern recognition.

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